Deep Autoencoder Neural Network Research Articles

Construction of Data Driven Decomposition Based Soft Sensors with Auto Encoder Deep Neural Network for IoT Healthcare Applications

The architecture of IoT healthcare is motivated towards the data-driven realization and patient-centric health models, whereas the personalized assistance is provided by deploying the advanced sensors. According to the procedures in surgery, in the emergency unit, the patients are monitored till they are stable physically and then shifted to ward for further recovery and evaluation. Normally evaluation done in ward doesn’t suggest continuous parameters monitoring for physiological condition and thus relapse of patients are common. In real-time healthcare applications, the vital parameters will be estimated through dedicated sensors, that are still luxurious at the present situation and highly sensitive to harsh conditions of environment. Furthermore, for real-time monitoring, delay is usually present in the sensors. Because of these issues, data-driven soft sensors are highly attractive alternatives. This research is motivated towards this fact and Auto Encoder Deep Neural Network (AutoEncDeepNN) is proposed depending on Health Framework in the internet assisting the patients with trigger-based sensor activation model to manage master and slave sensors. The advantage of the proposed method is that the hidden information are mined automatically from the sensors and high representative features are generated by multiple layer’s iteration. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like Hierarchical Extreme Learning Machine (HELM), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). It is found that the proposed AutoEncDeepNN method achieves 94.72% of accuracy, 41.96% of RMSE, 34.16% of RAE and 48.68% of MAE in 74.64 ms.

Building clustering for regional seismic response and damage analysis

This article presents a framework to cluster buildings into typologically similar groups and select indicator buildings for regional seismic response and damage analysis. The framework requires a robust database of buildings to provide high-level structural and site information of buildings. Here, a database of 234 reinforced concrete buildings with five or more above-ground stories in the central business district of Wellington, New Zealand, has been selected as the case study of this research. First, key structural and site parameters that contribute to the seismic demand, response, and damage of each building are extracted from the database. Extracted parameters comprise three numerical and five categorical attributes of each building, including the year of construction, height, period, lateral load resisting system, floor system, site subsoil class, importance level, and strong motion station. Next, two prominent unsupervised machine learning clustering approaches are utilized to cluster the mixed categorical and numerical building database: k-prototype on the mixed numerical and categorical database and k-means on principal components numerical subspace adopted from factor analysis of mixed data (FAMD). A novel autoencoder deep learning neural network is also designed and trained to convert the mixed data into a low-dimensional subspace called latent space and feed this into k-means for clustering. The proposed autoencoder method is demonstrated to be more effective at clustering buildings into useful typological clusters for seismic response and damage analysis based on multiple criteria from both data-science and engineering perspectives. The details of selected indicator buildings for each similar seismic vulnerability cluster are also represented.

Multimodal Emotion Recognition in Response to Oil Paintings.

Most previous affective studies use facial expression pictures, music or movie clips as emotional stimuli, which are either too simplified without contexts or too dynamic for emotion annotations. In this work, we evaluate the effectiveness of oil paintings as stimuli. We develop an emotion stimuli dataset with 114 oil paintings selected from subject ratings to evoke three emotional states (i.e., negative, neutral and positive), and acquire both EEG and eye tracking data from 20 subjects while watching the oil paintings. Furthermore, we propose a novel affective model for multimodal emotion recognition by 1) extracting informative features of EEG signals from both the time domain and the frequency domain, 2) exploring topological information embedded in EEG channels with graph neural networks (GNNs), and 3) combining EEG and eye tracking data with a deep autoencoder neural network. From the exper-iments, our model obtains an averaged classification accuracy of 94.72 % ± 1.47 %, which demonstrates the feasibility of using oil paintings as emotion elicitation material.

Improving the predictive response using ensemble empirical mode decomposition based soft sensors with auto encoder deep neural network

In industries, several key quality variables used in complex processes are immeasurable online and are not reliable because of the factors like complex environmental criteria, limited techniques for testing and high cost. Soft sensor technology has become known to solve these complexities. In industrial process, the key factors like redundancy, noise and dynamic features of data affect the accuracy of soft sensors. Thus, a predictive control approach is required which has to integrate improved methods used to detect the control signal that uses direction of structural motion. This paper proposes an innovative Ensemble Empirical Mode Decomposition Based Auto Encoder Deep Neural Network (EEMD-AEDNN) which combines the advantages of Ensemble Empirical Mode Decomposition and neural network bringing problems of mode-mixing from EEMD and false modes from neural network under control. Moreover, dynamic characteristics are captured which are distributed over time improving the modelling effects. The advantage is that noise and redundancy from actual data are removed and information loss is minimized. Furthermore, data are sequential introducing historical data for dynamic modelling. This goal is consistently achieved and thus the proposed model outperforms few standard approaches which are considered like Ensemble Empirical Mode Decomposition based Long Short-Term Memory neural network (EEMD-LSTM), Wavelet Neural Network with Random Time (WNNRT) and Ensemble Empirical Mode Decomposition-General Regression Neural Network (EEMD-GRNN). It is found that the proposed EEMD-AEDNN method achieves 94.22% of accuracy, 84.68% of RMSE, 75.34% of RAE and 54.42% of MAE in 86.5 ms.

Analysis of the Effectiveness of Various Machine Learning, Artificial Neural Network and Deep Learning Methods in Detecting Fraudulent Credit Card Transactions

A credit card is an important financial tool that has emerged in parallel with the developments in technology from the past to the present and has become an indispensable part of human life. The credit card has many advantages that can be listed as facilitating online shopping, providing installments in purchases, and preventing cash dependence. This is why the rate of use of credit cards worldwide is increasing day by day. On the other hand, there are some risks of the credit cards highlighted by security concerns. The fraudsters who access the identity and credit card information of the consumers through different means use it to shop online without the consumer’s knowledge and gain an unfair advantage. Therefore, it is crucial to eliminate this security vulnerability that the fraudsters exploit and to develop an effective solution to the customer victimization experienced by e-commerce companies due to the fraudulent credit card transactions. With this motivation, the performance of the methods from different research fields was examined to explore the solution space in detail in terms of the problem at hand within the scope of this study. For this purpose, three machine learning algorithms (K-Nearest Neighbor, Naive Bayes, Support Vector Machine), two artificial neural network algorithms (Binary Classifier, Autoencoder), and two deep learning algorithms (Deep Autoencoder and Deep Neural Network Classifier) were implemented. The effectiveness of the algorithms in question was tested with a famous dataset widely used in the literature. Experimental results showed that the Deep Neural Network Classifier outperformed the other algorithms used in this study and the best study ever reported in the literature in detecting fraudulent credit card transactions when accuracy and AUROC performance criteria were taken into account.

A multi–modal unsupervised fault detection system based on power signals and thermal imaging via deep AutoEncoder neural network

In this paper a multi-modal unsupervised Deep Learning based algorithm for fault detection is proposed. Such method is applied to real data from a testing procedure implemented on an industrial production line. Both thermal images and current and power measurements coming from industrial refrigerators are collected. The considered dataset is highly unbalanced with the vast majority of samples being healthy. Thermal images are processed via a Deep Convolutional neural network. The features extracted from the thermal images are thus merged to structured data of power, current and temperature. Therefore, a Deep Auto-Encoder is trained on the dataset to signal anomalies corresponding to faults in the refrigerators. Three different methods are trained and compared: (1) an automatic method in which an expert extracts relevant features from thermal images without using the image recognition module; (2) a semi-automatic method where the convolutional neural network is applied to regions of interest within the thermal images selected by an expert operator; (3) a fully automatic method in which the Deep convolutional network processes the whole thermal image without any human intervention. The three methods show comparable results with nevertheless slight differences.

Machine Learning-Assisted Discovery of Hidden States in Expanded Free Energy Space.

Collective variables (CVs) are crucial parameters in enhanced sampling calculations and strongly impact the quality of the obtained free energy surface. However, many existing CVs are unique to and dependent on the system they are constructed with, making the developed CV non-transferable to other systems. Herein, we develop a non-instructor-led deep autoencoder neural network (DAENN) for discovering general-purpose CVs. The DAENN is used to train a model by learning molecular representations upon unbiased trajectories that contain only the reactant conformers. The prior knowledge of nonconstraint reactants coupled with the here-introduced topology variable and loss-like penalty function are only required to make the biasing method able to expand its configurational (phase) space to unexplored energy basins. Our developed autoencoder is efficient and relatively inexpensive to use in terms of a priori knowledge, enabling one to automatically search for hidden CVs of the reaction of interest.

Tomographic reconstruction from planar thermal imaging using convolutional neural network

In this study, we investigate perspectives for thermal tomography based on planar infrared thermal images. Volumetric reconstruction of temperature distribution inside an object is hardly applicable in a way similar to ionizing-radiation-based modalities due to its non-penetrating character. Here, we aim at employing the autoencoder deep neural network to collect knowledge on the single-source heat transfer model. For that purpose, we prepare a series of synthetic 3D models of a cylindrical phantom with assumed thermal properties with various heat source locations, captured at different times. A set of planar thermal images taken around the model is subjected to initial backprojection reconstruction, then passed to the deep model. This paper reports the training and testing results in terms of five metrics assessing spatial similarity between volumetric models, signal-to-noise ratio, or heat source location accuracy. We also evaluate the assumptions of the synthetic model with an experiment involving thermal imaging of a real object (pork) and a single heat source. For validation, we investigate objects with multiple heat sources of a random location and temperature. Our results show the capability of a deep model to reconstruct the temperature distribution inside the object.

Deep convolutional autoencoder augmented CFD thermal analysis of bearings with inter pad groove mixing

The treatment of thermal mixing in inter pad grooves of a fluid film bearing is essential due to its influence on the heat transfer with the rotating shaft and stationary bearing. Lower fidelity models that either neglect or over approximate thermal groove mixing may lead to premature bearing or machinery failure, most commonly from babbitt thermally induced fatigue. Conventional models rely on bulk flow and thermal analyses yielding a single temperature at the groove outlet into the pad inlet. The high uncertainty of this approach carries over into downstream predictions for bearing life, stiffness and damping, and machinery vibration predictions. Contrary to a uniform temperature, CFD-Conjugate heat transfer studies reveal large gradient temperature distributions varying in both the radial and axial directions at the groove outlet, especially with jet lubrication implemented with multiple nozzles. These distributions vary continuously with time as the spinning shaft and bearing pads vibrate. A direct CFD simulation thus becomes computationally prohibitive.The present work introduces a novel approach which yields highly detailed lubricant temperature distributions at the pad inlets in a computationally economical manner. This is implemented with a surrogate groove model via a deep convolutional autoencoder neural network based on CFD (Computational Fluid Dynamics) data. The trained Convolutional Neural Network (CNN) shows excellent prediction capability for 2D temperature distribution at a circumferential groove outlet. The trained CNN is combined with a rotor-bearing model, and the combined model is verified by full CFD results and experimental data. In addition, this approach is expanded to include various oil injection types, illustrating their detailed heat transfer to the rotating shaft and bearing.

A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems. Many issues in this field still unsolved, so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data. Such a phenomenon is referred to as the “curse of dimensionality” that affects traditional techniques in terms of both accuracy and performance. Thus, this research proposed a hybrid model based on Deep Autoencoder Neural Network (DANN) with five layers to reduce the difference between the input and output. The proposed model was applied to a real-world gas turbine (GT) dataset that contains 87620 columns and 56 rows. During the experiment, two issues have been investigated and solved to enhance the results. The first is the dataset class imbalance, which solved using SMOTE technique. The second issue is the poor performance, which can be solved using one of the optimization algorithms. Several optimization algorithms have been investigated and tested, including stochastic gradient descent (SGD), RMSprop, Adam and Adamax. However, Adamax optimization algorithm showed the best results when employed to train the DANN model. The experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%, F1-score of 0.9649, Area Under the Curve (AUC) rate of 0.9649, and a minimal loss function during the hybrid model training.

A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors.

Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain good fault classification accuracy, yet domain expertise and a fair amount of labeled data are important for better features. This paper proposes a deep auto-encoder (DAE) and convolutional neural network (CNN)-based bearing fault classification model using motor current signals of an induction motor (IM). Motor current signals can be easily and non-invasively collected from the motor. However, the current signal collected from industrial sources is highly contaminated with noise; feature calculation thus becomes very challenging. The DAE is utilized for estimating the nonlinear function of the system with the normal state data, and later, the residual signal is obtained. The subsequent CNN model then successfully classified the types of faults from the residual signals. Our proposed semi-supervised approach achieved very high classification accuracy (more than 99%). The inclusion of DAE was found to not only improve the accuracy significantly but also to be potentially useful when the amount of labeled data is small. The experimental outcomes are compared with some existing works on the same dataset, and the performance of this proposed combined approach is found to be comparable with them. In terms of the classification accuracy and other evaluation parameters, the overall method can be considered as an effective approach for bearing fault classification using the motor current signal.

Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease

Coronavirus disease 2019 pandemic spreads rapidly and requires an acceleration in the process of drug discovery. Drug repurposing can help accelerate the drug discovery process by identifying new efficacy for approved drugs, and it is considered an efficient and economical approach. Research in drug repurposing can be done by observing the interactions of drug compounds with protein related to a disease (DTI), then predicting the new drug-target interactions. This study conducted multilabel DTI prediction using the stack autoencoder-deep neural network (SAE-DNN) algorithm. Compound features were extracted using PubChem fingerprint, daylight fingerprint, MACCS fingerprint, and circular fingerprint. The results showed that the SAE-DNN model was able to predict DTI in COVID-19 cases with good performance. The SAE-DNN model with a circular fingerprint dataset produced the best average metrics with an accuracy of 0.831, recall of 0.918, precision of 0.888, and F-measure of 0.89. Herbal compounds prediction results using the SAE-DNN model with the circular, daylight, and PubChem fingerprint dataset resulted in 92, 65, and 79 herbal compounds contained in herbal plants in Indonesia respectively.

IoT network security using autoencoder deep neural network and channel access algorithm

Abstract Internet-of-Things (IoT) creates a significant impact in spectrum sensing, information retrieval, medical analysis, traffic management, etc. These applications require continuous information to perform a specific task. At the time, various intermediate attacks such as jamming, priority violation attacks, and spectrum poisoning attacks affect communication because of the open nature of wireless communication. These attacks create security and privacy issues while making data communication. Therefore, a new method autoencoder deep neural network (AENN) is developed by considering exploratory, evasion, causative, and priority violation attack. The created method classifies the transmission outcomes used to predict the transmission state, whether it is jam data transmission or sensing data. After that, the sensing data is applied for network training that predicts the intermediate attacks. In addition to this, the channel access algorithm is used to validate the channel for every access that minimizes unauthorized access. After validating the channel according to the neural network, data have been transmitted over the network. The defined process is implemented, and the system minimizes different attacks on various levels of energy consumption. The effectiveness of the system is implemented using TensorFlow, and the system ensures the 99.02% of detection rate when compared with other techniques.

An Early Breast Cancer Detection System Using Stacked Auto Encoder Deep Neural Network with Particle Swarm Optimization Based Classification Method

The demand in breast cancer’s early detection and diagnosis over the last few decade has given a new research avenues. For an individual who is suffered from breast cancer, a successful treatment plan can be specified if early stage diagnosis of non-communicable disease is done as stated by world health organization (WHO). Around the world, mortality can be reduced by cure disease’s early diagnosis. For breast cancer’s early detection and to detect other abnormalities of human breast tissue, digital mammogram is used as a most popular screening method. Early detection is assisted by periodic clinical check-ups and self-tests and survival chance is significantly enhanced by it. For mammograms (MGs), deep learning (DL) methods are investigated by researchers due to traditional computer-aided detection (CAD) systems limitations and breast cancer’s early detection’s extreme importance and patients false diagnosis high impact. So, there is need to have a noninvasive cancer detection system which is efficient, accurate, fast and robust. There are two process in proposed work, Histogram Rehabilitated Local Contrast Enhancement (HRLCE) technique is used in initial process for contrast enhancement with two processing stages. Contrast enhancements potentiality is enhanced while preserving image’s local details by this technique. So, for cancer classification, Particle Swarm Optimization (PSO) and stacked auto encoders (SAE) combined with framework based on DNN called SAE-PSO-DNN Model is used. The SAE-DNN parameters with two hidden layers are tuned using PSO and Limited-memory BFGS (LBFGS) is used as a technique for reducing features. Specificity, sensitivity, normalized root mean square erro (NRMSE), accuracy parameters are used for evaluating SAE-PSO-DNN models results. Around 92% of accurate results are produced by SAE-PSO-DNN model as shown in experimentation results, which is far better than Convolutional Neural Network (CNN) as well as Support Vector Machine (SVM) techniques.

A Scheme with Acoustic Emission Hit Removal for the Remaining Useful Life Prediction of Concrete Structures.

In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure’s failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration process is portrayed by the health indicator (HI), which is automatically constructed from raw AE data with a deep neural network pretrained and fine-tuned by a stacked autoencoder deep neural network (SAE-DNN). For the deep neural network structure to perform a more accurate construction of health indicator lines, a hit removal process with a one-class support vector machine (OC-SVM), which has not been investigated in previous studies, is proposed to extract only the hits which matter the most to the portrait of deterioration. The new set of hits is then harnessed as the training labels for the deep neural network. After the completion of the health indicator line construction, health indicators are forwarded to a long short-term memory recurrent neural network (LSTM-RNN) for the training and validation of the remaining useful life prediction, as this structure is capable of capturing the long-term dependencies, even with a limited set of data. Our prediction result shows a significant improvement in comparison with a similar scheme but without the hit removal process and other methods, such as the gated recurrent unit recurrent neural network (GRU-RNN) and the simple recurrent neural network.

Interpretable uncertainty estimation for automated triage of 12-lead electrocardiogram using deep convolutional neural networks

Abstract Background Interpretation of electrocardiograms (ECGs) using artificial intelligence (AI) has shown very promising results recently, such as comprehensive triage of ECGs. Despite this progress, remarkably few of these AI algorithms have been implemented in clinical practice. Mayor challenges before safe widespread implementation is possible, are uncertainty estimation and interpretability. Without these, algorithms are forced to always provide a diagnosis or prediction. When the algorithm has never seen such an ECG before or when it is noisy or contains a difficult or ambiguous case, this could lead to unrecognized erroneous results. Purpose We propose a novel deep neural network (DNN) architecture that is able to overcome both the interpretability and uncertainty estimation challenges in automated diagnosis of ECGs. Methods A dataset with 320.000 raw 10s 12-lead ECGs was used to train and validate the DNN in separate 90:10 splits. All ECGs were interpreted by a physician and these free-text annotations were translated into 6 categories from normal to acute based on how quickly a cardiologist needs to be consulted. Using a novel variational autoencoder DNN, the ECG is compressed into a latent space with 64 parameters. In the compressed space, each category has a separate distribution, instead of a single distribution for the whole dataset. For clinical use, the ECG is represented in the compressed space and the category prior distribution that is the most likely to produce the input ECG representation is the predicted category. The smaller the probability of generating the data point under any of the known categories, the more uncertain the prediction. The compressed space is reduced using principal component analysis (PCA) to visualize where the input is represented in the compressed landscape. Performance of the uncertainty estimation is assessed in the validation set by ranking the uncertainty in 10 bins from highest to lowest and comparing with the accuracy. Results We demonstrate that the accuracy of the algorithm decreases for ECGs where the model is more uncertain about its predictions (Figure 1). Prediction accuracies range from 90% for the ECGs where the algorithm is most certain, to 50% where it is most uncertain. The PCA of the latent space appears to be able to visually represent the categories in the latent space (Figure 2). Moreover, it indicates whether the new ECG belongs to a known category, the algorithm is doubting between categories or the ECG belongs to none of the categories. Conclusion The proposed novel method is promising in estimating uncertainty in an interpretable way, which could allow for safer implementation of AI-based ECG interpretation. Further research is needed to improve the baseline performance of the model, to compare to existing methods and to examine the detection of noisy or new ECGs. The interpretability can be further extended to highlight the uncertain regions on the ECGs. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): ZonMw Figure 1. Uncertainty versus accuracyFigure 2. Representation of ECG

DFTSA-Net: Deep Feature Transfer-Based Stacked Autoencoder Network for DME Diagnosis

Diabetic macular edema (DME) is the most common cause of irreversible vision loss in diabetes patients. Early diagnosis of DME is necessary for effective treatment of the disease. Visual detection of DME in retinal screening images by ophthalmologists is a time-consuming process. Recently, many computer-aided diagnosis systems have been developed to assist doctors by detecting DME automatically. In this paper, a new deep feature transfer-based stacked autoencoder neural network system is proposed for the automatic diagnosis of DME in fundus images. The proposed system integrates the power of pretrained convolutional neural networks as automatic feature extractors with the power of stacked autoencoders in feature selection and classification. Moreover, the system enables extracting a large set of features from a small input dataset using four standard pretrained deep networks: ResNet-50, SqueezeNet, Inception-v3, and GoogLeNet. The most informative features are then selected by a stacked autoencoder neural network. The stacked network is trained in a semi-supervised manner and is used for the classification of DME. It is found that the introduced system achieves a maximum classification accuracy of 96.8%, sensitivity of 97.5%, and specificity of 95.5%. The proposed system shows a superior performance over the original pretrained network classifiers and state-of-the-art findings.

Detection of neurodegenerative disease in brain using region splitting based segmentation with deep unsupervised neural networks

AbstractUnsupervised segmentation is a significant pre‐processing task in various computer visualization processes. However, recently used unsupervised segmentation methods are very sensitive to few of the parameters like number of segmentation or numerous training and more inference complication. Neurodegenerative disorders namely Parkinson's and Alzheimer's, add up to main aspects to longer disability and has grown to be a more serious concept in the developed nations. At present, there does not exist efficient therapies. Earlier diagnosis along with avoiding misdiagnosis significantly assists in ensuring a good quality for patient's life. Hence, adopting computer‐aided‐diagnosis tools provides clinical assistance. This paper focus on detecting neurodegenerative disease from CT brain images by segmentation through region splitting based segmentation with deep unsupervised neural networks (RSS‐DUNN).Auto encoder deep neural network is constructed with reconstruction process of input image, which further improves the accuracy. The performance of the proposed model is evaluated by comparing it with two standard methods in terms of accuracy, precision, recall, Jaccard similarity index (JSI) and dice similarity coefficient (DSC). As a result, it achieves 70.4% of accuracy, 70.8% of precision, 75.8% of recall, 73.6% of JSI and 81% of DSC.

Active-Learning-Based Generative Design for the Discovery of Wide-Band-Gap Materials

Active learning has been increasingly applied to screening functional materials from existing materials databases with desired properties. However, the number of known materials deposited in the popular materials databases such as ICSD and Materials Project is extremely limited and consists of just a tiny portion of the vast chemical design space. Herein we present an active generative inverse design method that combines active learning with a deep variational autoencoder neural network and a generative adversarial deep neural network model to discover new materials with a target property in the whole chemical design space. The application of this method has allowed us to discover new thermodynamically stable materials with high band gap (SrYF$_5$) and semiconductors with specified band gap ranges (SrClF$_3$, CaClF$_5$, YCl$_3$, SrC$_2$F$_3$, AlSCl, As$_2$O$_3$), all of which are verified by the first principle DFT calculations. Our experiments show that while active learning itself may sample chemically infeasible candidates, these samples help to train effective screening models for filtering out materials with desired properties from the hypothetical materials created by the generative model. The experiments show the effectiveness of our active generative inverse design approach.

A deep neural network approach to QRS detection using autoencoders

Objective:In this paper, a stacked autoencoder deep neural network is proposed to extract the QRS complex from raw ECG signals without any conventional feature extraction phase. Methods:A simple architecture has been deeply trained on many datasets to ensure the generalization of the network at inference. Results:The proposed method achieved a QRS detection accuracy of 99.6% using more than 1042000 beats which is competitive with all state-of-the-art QRS detectors. Moreover, the proposed method produced only 0.82% of Detection Error Rate using six unseen datasets containing more than 1470000 beats. Thus confirms the high performance of our method to detect QRSs. Conclusion:Stacked autoencoder neural networks are very effective in QRS detection. At inference, our algorithm processes 1042309 beats in less than 25.32 s. Thus, it is favorably comparable with state-of-the-art deep learning methods. Significance:The stacked autoencoder is an efficient tool for QRS detection, which could replace conventional systems to help practitioners make fast and accurate decisions.